Video Recognition. We evaluate CMAC on two video action recognition benchmarks of UCF101 and HMDB51.
The results are reported in Table REF , which shows that CMAC obtains impressive results on both UCF101 and HMDB51.
It should be noted that CMAC is a computation-friendly method.
Particularly, GDT [1]} utilizes 6 times of GPU cost and batch size than CMAC, while CMAC surpasses GDT about \(1.0\%\)  on UCF101 and \(1.1\%\)  on HMDB51.
Compared to AVID [2]}, CMAC also adopts much smaller batch size and fewer epochs to obtain \(2.8\%\)  improvement on UCF101 and \(0.3\%\)  on HMDB51.
The results reveal that the bidirectional local correspondence is effective in boosting the visual representation learning.
After teaching the visual encoder where to focus on, it becomes easier to understand the visual content and distinguish different video instances.
Thus, CMAC requires fewer batch size and epoch to surpass most of the existing methods.
